Assessing the Impact of Experienced Project Team Members in Green Building Projects

Project experience is generally regarded as highly valuable in the architecture, engineering and construction industry. This is also true for green building projects, which often need to deal with new building technologies and processes. This paper attempts to study the importance of experienced project team members for successful planning and executing of green building projects. Certified LEED green building projects in Canada were studied in this research. Project information, project team information, green building certification grade, and certification year were collected and analyzed using a link analysis technique. Organisations that have been involved in multiple green building projects and their inter-organisational interactions were identified. The results show that projects certified with higher green building certification grades often involve more experienced project team members, and that working with experienced team members can reinforce mutual experience as compared with working with less experienced member.International Council for Research and Innovation in Building and Construction (CIB

A web service framework for environmental and carbon footprint monitoring in construction supply chains

With the growing environmental concerns, green supply chain management (GSCM) is gaining significant attention in the construction industry. Tracking and monitoring the environmental effects brought forth by the participating members along a supply chain is important to GSCM. The GreenSCOR model developed by the Supply Chain Council provides a generic framework for measuring the total carbon footprint and environmental footprint in a supply chain. The model is based on the Supply Chain Operations Reference (SCOR) model, which represents a supply chain network in a hierarchically structured manner. This paper describes the GreenSCOR framework and its potential application to the construction industry. This paper also presents a web services approach to incorporate the GreenSCOR model to the implementation of collaborative information systems. Each process element in the SCOR model is represented and delivered as individual web service units, which can be reused and integrated using standard web services technologies. The service units are combined and managed in a prototype web service collaborative framework, called SC Collaborator, which is designed and developed for supporting construction supply chain management. An illustrative example is presented to demonstrate the implementation of the GreenSCOR-based SC Collaborator framework

A framework for synthetic image generation and augmentation for improving automatic sewer pipe defect detection

Sewer pipes are essential infrastructure for discharging wastewater. Regular pipe inspection is necessary to prevent malfunction of sewer systems, for which closed-circuit television (CCTV) crawlers are commonly used to capture images of the pipe interior. As manual assessment of pipe condition is labor-intensive and time-consuming, automated defect detection using computer vision and deep learning has been increasingly studied in recent years. However, deep learning approaches require large amount of annotated data for model training. Data collection in underground sewer pipes is expensive and difficult since they are inaccessible without the use of an inspection robot. Meanwhile, ground-truth annotation needs to be accurate and consistent, requiring massive time and expertise. This paper proposes a framework for synthetic data generation and augmentation to address the data shortage problem for sewer pipe defect detection. First, synthetic images of sewer pipes are generated by 3D modeling and simulation in virtual environment. The quality of the generated images is then enhanced using style transfer with reference to real inspection images. In addition, a contrastive learning module is developed to further improve the deep learning process for defect detection. Experiment results show that the average precision (AP) of the defect detection model is improved by 2.7% and 4.8% respectively after adding style-transferred synthetic images and applying the contrastive module. When both methods are applied, the AP of the model is boosted by 7.7%, from 22.22% to 23.92%, indicating the effectiveness of our proposed approaches. This study is expected to alleviate the burden on data collection and annotation for applying deep learning models in defect detection

Online Geometry Quality Management during Directed Energy Deposition using Laser Line Scaner

Additive manufacturing (AM) is a powerful and promising manufacturing technology due to its advantages of material saving, mass customization and small-quantity production of custom-designed products. However, current situation of lacking quality management in 3D printing process is the key barrier of adopting this advanced technology. Geometry inaccuracy of 3D printed components is one of the main quality problems for AM, especially when the final product requires high precision in its geometry. In this study, an online geometry quality management method for continuous monitoring during the direct energy deposition (DED) process was developed using a laser line scanner. Our proposed methodology comprises: (1) real-time track-by-track scanning of multi-layer single-track component, (2) online geometry extraction of multi-layer single-track component during printing process, and (3) online plotting and comparison of the as-designed and as-built models.Mechanical Engineerin

Automated sewer pipe defect tracking in CCTV videos based on defect detection and metric learning

© 2020 Elsevier B.V. Computer vision techniques are widely studied for automating the interpretation of sewer pipe inspection videos, yet previous studies mainly focus on defect detection and segmentation of individual images, which cannot identify if the defect is the same one across consecutive video frames (i.e. track the defect). Nevertheless, the number of unique defects in the video is required for evaluating the pipe condition. This paper proposes a framework for tracking multiple sewer defects in CCTV videos based on defect detection and metric learning. First, a deep learning -based defect detection model and a metric learning model is developed and trained respectively using with our sewer datasets. Then, using the detections and their features from the trained models as inputs, the tracking module predicts tracks by Kalman filter and associates tracks based on defect motion, appearance features, and defect types. Our experiments demonstrate the framework is able to track sewer defects in CCTV videos with a decent IDF1 score of 57.4%. We notice that tracking performance can be influenced by the detection accuracy and configurations of the metric learning module. By analyzing the tracking results based on different weights of the distance metrics, we find that assigning larger weights to appearance and defect class distance metrics tends to increase IDF1 score, while larger motion distance weight may degrade tracking accuracy. The proposed framework contributes by tracking multiple sewer defects, which can assist with counting unique defects in inspection videos